jefflai108 / Contrastive-Predictive-Coding-PyTorch

Contrastive Predictive Coding for Automatic Speaker Verification

Contrastive-Predictive-Coding-PyTorch

This repository contains (PyTorch) code to reproduce the core results for:

• Representation Learning with Contrastive Predictive Coding
• Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

If you find the code useful, please cite

@article{lai2019contrastive,
  title={Contrastive Predictive Coding Based Feature for Automatic Speaker Verification},
  author={Lai, Cheng-I},
  journal={arXiv preprint arXiv:1904.01575},
  year={2019}
}

Getting Started

./src/model/model.py contains the CPC models implementation, ./src/main.py is the code for training the CPC models, ./src/spk_class.py trains a NN speaker classifier, ./ivector/ contains the scripts for running an i-vectors speaker verification system.

An example of CPC and speaker classifier training can be found at

./run.sh

CPC Models

CDCK2: base model from the paper 'Representation Learning with Contrastive Predictive Coding'.
CDCK5: CDCK2 with a different decoder.
CDCK6: CDCK2 with a shared encoder and double decoders.

Experimental Results

A. CPC Model Training

CPC model ID	number of epoch	model size	dev NCE loss	dev acc.
CDCK2	60	7.42M	1.6427	26.42
CDCK5	60	5.58M	1.7818	22.48
CDCK6	30	7.33M	1.6484	28.24

B. Speaker Verificaiton on LibriSpeech test-clean-100 (Average Pooling)

Note: 1st trial list and 2nd trial list

Feature	Feature Dim	Summarization	LDA Dim	1st EER	2nd EER
MFCC	24	average pooling	24	9.211	13.48
CDCK2	256	average pooling	200	5.887	11.1
CDCK5	40	average pooling	40	7.508	12.25
CDCK6	256	average pooling	200	6.809	12.73

C. CPC applied with PCA

Feature w PCA	Original Feature	PCA Dim	PCA Variance Ratio
CDCK2-36	CDCK2	36	76.76
CDCK2-60	CDCK2	60	87.40
CDCK5-24	CDCK5	24	93.39

D. Speaker Verificaiton on LibriSpeech test-clean-100 (i-vectors)

Feature	Feature Dim	Summarization	1st EER	2nd EER
MFCC	24	i-vectors	5.518	8.157
CDCK2-60	60	i-vectors	5.351	9.753
CDCK5-24	24	i-vectors	4.911	8.901
CDCK6-60	60	i-vectors	5.228	9.009
MFCC + CDCK2-36	60	i-vectors	3.62	6.898
MFCC + CDCK5-24	48	i-vectors	3.712	6.962
MFCC + CDCK6-36	60	i-vectors	3.691	6.765

E. DET Curves of CPC and MFCC Fusion for i-vectors Speaker Verification

Authors

Cheng-I Lai.

If you encouter any problem, feel free to contact me.